Decision Tree Classification in Python (from scratch!)
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- เผยแพร่เมื่อ 20 ม.ค. 2021
- This video will show you how to code a decision tree classifier from scratch!
#machinelearning #datascience #python
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This is really awesome!!! Stay blessed and keep producing these great learning videos!
This was such a thorough yet approachable explanation. Thanks!
Great work! Thanks for sharing. As you said it "So satisfying to see a model coded from the scratch perform so well" :)
Hey! I can easily say that your channel will be worldwide sooner or later! Never compromise from the quality. Great job.
Thanks a lot mate :D :D
I'm a few months late, but I realized that this specific video is just a slight modification of google's "Let’s Write a Decision Tree Classifier from Scratch".
you are amazing! hope your channel goes along and more and more popular!
There is just a WOW factor. I had been looking for explanation and implementation from the scratch in this way. Thumbs up to you. Thanks!
Glad to hear that! :D
For anyone wondering. this is not ID3 because ID3 only works for data sets with categorical attributes, its neither C4.5 because that one generates possibly a general tree and handles both numeric and categorical attributes, and here the algorithm presented generates a binary tree, most close to CART, but CART its for regression and classification, so the guy developed his own algorithm
Great explanation. I needed the Decision Tree for my research.
This is a great playlist! Keep up the good work
Can't believe I'm just finding this channel!
I've been working as a DS for a few years now, but my company is finally delving into ML workflows, and I need a refresher from my undergrad. Turns out, these videos are even more useful than said undergrad!
Thanks, nerd. I'll be tying this into other courses as explainers, great videos
It's a great tutorial. I have always been struggling to understand these topics. And, now I feel I discovered the right path to get into it. Please keep it up. In addition, it would be much meaningful if the codes you wrote are available for download. Many thanks!
Fantastic video, just like the previous one. Great teacher. Thanks.
Thanks! In your previous video it wasn't explained how you use information gain to subsequently calculate the best conditions in each node. This explains it very well: recursion!
Excellent explanation. The coding is made so simple with your perfect explanation. Thank you :)
You put a lot of work in this video, thank you! Subscribed :)
Thanks for the sub!
This is so great...! Just the right video to get me started with Decision Tree Classifier..!!! Keep going, would like to see more videos on other ML algorithms...!!!
For sure!
Hello from the future, Normalized Nerd! These videos are excellent and incredibly straightforward. Thank you for your contribution and sharing of your knowledge
Glad you like them!
Beautiful explanation. Thank you!
Thanks bro for clear explanation and well written code
Normalized Nerd please come back we love ur videos !
Very clear and intuitive! Thanks
Glad it was helpful!
❤love the content keep it up brother… proud of u
Wow!! I had been looking for this!
That's great! Keep supporting :D
Great work, really appreciate these videos!
Glad you like them!
Thanks for sharing! I can't wait to start coding!❤
Please continue uploading more information videos like this. 👍👍
thank you, man!! , You saved it, Nice and easy Explanation.
You're the best! Thank you for the great tutorial!
Thanks for the video, this would help me with my btech project.
Gracias Mil !!! Este video se complementa muy bien con el anterior. De otro lado, tienes algún tutorial sobre Regression Tree?
your videos are awesome bro!!!
it is real a well explained video, thanks a lot!!!
Perfect explanation!
Brilliant video!!
Very informative.. thanks for sharing
Thank you teacher! I swear you are the best teacher in the world!
Great videos, keep up the good work
Thanks!! :D
Awesome, man!
It is really a great piece of work.
Hi, thank you very much for this video. Is there a way to turn the tree we obtain into a more readable graphic representation?
Thanks, Nice tutorial
Thank you..Well explained..
Absolutely amazed
well explained! Thank you!
Glad to hear that!
This is good stuff, thanks!
You're welcome!
final part seems a little rush but it's fine.
would appreciate if you explain how the model corelates with the flower dataset
Can you show us on how you can calculate the entropy and the gini within your code?
u r doing ground breaking work!!! code refer kru kya??
LOVE THIS
AMAZING, thanks.
The best tutorial👍
Very nice!
Hi Great Video!
Just wanted to quickly ask if this was based on the zero rule algorithm or CART algorithm. This scratch implementation of decision tree differs from the sklearn's decision tree on my dataset. This implementation trained on my dataset gives me 78% accuracy whereas sklearn's decision tree gives me 95% accuracy. Any ideas what the difference between the 2 implementations could be?
Thank you for this video
Very helpful video
I ask Chat GPT about something and it broad me here. Man, thanks for this guy and chat GPT, now I got a purpose to do.
did you use id3 algorithm for the classification ?
TypeError Traceback (most recent call last)
in ()
1 classifier = DecisionTreeClassifier(min_samples_split=3, max_depth=3)
----> 2 classifier.fit(X_train,Y_train)
3 classifier.print_tree()
Can you tell me what is the motive of coding decision tree from scratch.
Well, we can use the sklearn algo. for it. But, did you do it for just upskilling in python Or,
The coded decision tree is better than the sklearn library decision tree.
Because, I think the basic algo. of sklearn is also based on the recursive pattern for creating nodes.
So, please tell me, what are the benefits of making our own decision tree
How do you do those visualizations? They are cool
Great Job sir. Very usefull video
So nice of you
Greate explaination!!
Thanks!!
thank you !
Hey there.. just wanted to ask you if there are more than two attributes, will Machine Learning check information gain from each attribute for every value of attribute..
Can I choose 'entropy' for information gain in this model.
hey, is this for C4.5, ID3, or Hunt's algoritm? thx
I would have loved if you could have gone through the code in more details e.g. explain more thoroughly what each line does and also include an arrow to show in which line of code you are when explaining it. It owuld make it a lot easier for beginners like me to understand your video.
@Normalized Nerd how even after using the same code my accuracy is coming 100%?
Can I use this code for different values of max_depth ?? Actually I don't want to do any pruning here
thank you so much
thank you buddy
Imagine paying every month over 100 euros to learn a course based on AI and 30 mins on your vids I've already learned significantly more then I have 2 and a half months with those teachers... Sad world where the teachers hired (A person pretty much directly responsible for future generations) are absolute garbage and 100% don't deserve their pay check with the effort and enthusiasm they put in class.
Regarthless, your vids are trully amazing and easy to follow! Good job man and hope these positive comments made your work worth it :) I know it did to me
so what does X_2
awesome
Indians at their Best!!! Thanks for such a great content bro!
Thank you for this tutorial
I have a question
Why the program give me
Invalid DecisionTreeClassifier
This guy is 3blue1brown but for ML. Thank you!
where do i run these commands
how to vizualise the above code using graphviz
Is it C4.5 decision tree sir?
Can anyone help me out ,I'm getting error like couldn't able to convert int value into float from datasheet
Please provide source for dataset also
Good video and the animation is similar to 3blues 1brown, how did you make this video animation I wonder.
I used Manim (an opensource python library) created by 3blue1brown himself!
@@NormalizedNerd yes it is possible but have to write a lot of code
Next time please use something besides the iris dataset, if you can. Thanks for the video though.
Well, this was the first time I touched iris in this channel haha...will use more diverse datasets
Thank you but my accuracy_score is 0.36.. :(
Can some explain what is feature_index?
for Node, it seems to simply identify the depth of the current node.
in 'get_best_split' it's an index for the loop as it iterates through the range(num_features).
@@alfraelich Okay the the min depth value is given to to the feature index?
@@secondarypemail7181 It's the index of 'range(num_features)' from the 'get_best_split'. This is passed as a return to the calling function, 'build_tree', just before it's used as an argument in the call to 'Node()' in the __init__
if you look at the 'print_tree' function, it's used there in the print statements to show 'X_' value, as well as in the 'split' function to ensure the value is below the threshold. the range is 4, which gives us the 0-3 we see in the printout of the tree.
try plugging it print statements throughout to see where the value changes and to what.
I hope that helped.
13:48
Good explanation of the Decision Tree but '__init__' is not a constructor.
Yes, it's not. Mentioned it for the beginners.
We need you to trace the code line by line by hand with real data using a very small data set to truly understand what is happening
Pandas is just for the data set. He is doing it from scratch.
Good video but a bit hard to follow sometimes on phone when the code is not pointed out with a cursor, cant see exactly what line you are referring to sometimes
Bhai tumi Bangali?
Ha :)
bhai code to dedo yar
Value is pronounced with a /y/ sound, /v ae l y uw/, not as you pronounce it /v ae l uw/.
Hello people from the future 😎
What do you mean
"Building from scratch"....proceeds to use pandas
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You save my life, thank you
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AND I LOVE YOUR CONTENT!!!!
AWESOME!!!
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You are not coding it from scratch. You are just scrolling through a slide which has screenshots of a pre-written jupyter notebook code while explaining obvious stuff that anyone can read just by looking over the class, function and variable names. 0/10